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BØK356_1

Statistics and social science methodology

This is the study programme for 2020/2021.


"The world’s most valuable resource is no longer oil, but data." The Economist (2017). The global economy is increasingly driven by knowledge accumulation, transfer, and utilization. However, how can data be turned into information and eventually into knowledge? This question is not restricted to empirical researchers, analysts and data scientists. It is also crucial for business managers. Data sourcing, information processing and analyzing these are at the core of modern managerial tasks. This requires that managers know how to collect, process, and crucially, analyze small and large data sets. They also need to know how to transform the results of their analyses into easy-to-understand visualizations and informative presentations to share their insights with others. The course BØK356 focuses on analytical skills that will help students to work with given and structured data. In addition to basic statistical concepts, students will also learn to work with the empirical software R.

Learning outcome

Knowledge
On completion of the course, students will gain knowledge in:
  • Basic programming in R
  • Describing and comparing distributions
  • Fundamental concepts of bivariate statistics
  • Basic binary and multivariate regression analysis
  • Regression diagnostics

Skills
Upon completion of this course, students will be able to:
  • Import and export data into R
  • Use R to construct different measures and variables
  • Condense information into meaningful indices and metrices
  • Assess and compare empirical variables
  • Conduct basis econometric analyses in bivariate and multivariate settings.

Contents

Typical subject areas covered are:
  • Introduction into R
  • Descriptive statistics such as distributional characteristics (mean, median, …)
  • Bivariate statistics such as group comparisons (t-test etc.)
  • Correlation analysis
  • Bivariate & multivariate OLS regression (OLS)

Expectations: 280 ECTS work hours divided on lectures, in-class and out-of-class work, exercises and independent study.
Central literature
  • Grolemund, G., Wickham, H. 2017. "R for Data Science", O'Reilly Media
  • James, Witten, Hastie and Tibshirani. 2013. "An Introduction to Statistical Learning: with Applications in R"

Required prerequisite knowledge

None.

Exam

Weight Duration Marks Aid
Written exam1/1 A - F
The final grade is based on a final individual exam accounting for 100% of the grade.

Coursework requirements

Innleveringsoppgaver, Tilstedeværelse 80%
The course involves a number of pass/fail projects focusing on the use of R, which are compulsory to all students and are required to be admitted to the final exam.
Students are required to attend at least 80% of the classes. Students failing the exam will be allowed to participate in the course in the subsequent year. Exemptions from this rule will be decided on an individual basis.

Course teacher(s)

Course coordinator
Tom Brökel

Method of work

In this course, you will learn through the mixture of traditional lectures, exercises, and individual study. Lectures will provide the basic understanding of using R, on working with data, and present a variety of statistical concepts. In class, exercises are problem-based and students will learn by working on small exercises. Students are required to obtain necessary knowledge by self-studying a variety of materials including videos, textbook chapters, and lecture slides. Moreover, they are expected to practice the use of R through completing exercises, re-programming examples, and self-studying the course material.

Course assessment

Students will have the opportunity to give feedback on the course first in an early dialogue, and then in a written course evaluation at the end of the course.

Literature

Literatur will be published as soon as it has been prepared by the course coordinator/teacher


This is the study programme for 2020/2021.

Sist oppdatert: 22.09.2020

History